Temporal data refers to data samples collected over time, for example, laboratory measurements of a patient over a 5 year period.
On the other hand, longitudinal data is a specific type of temporal data obtained from multiple, repeatedly measured subjects, and the data contains correlations among the observations within a single subject and across multiple subjects. For example, a clinical drug trial with several patients whose lab measurements are collected on several occasions over a 5 year period.
Temporal and longitudinal studies are crucial in many fields, such as healthcare, economics, and social sciences, to name a few.
In order for a VAE to be applicable to diverse real-life datasets, we require a model that: does not ignore the possible correlations between the latent embeddings across samples; does not assume that all the features in the data are modelled using the same type of distribution; and can handle missing observations while ensuring efficient computations.
Some of the work on GP prior VAEs proposed approaches for modelling correlations between latent embeddings and handling missing observations [1]}, [2]}, while Casale et al.[3]} proposed a method to learn the auxiliary covariates from the data.
